The relationship between low socioeconomic status and worse health outcomes may be partly explained by the fact that individuals of low SES are much more likely to be uninsured. This study will examine the independent effect (i.e. after adjustment for covariates) of prolonged lack of insurance and acute changes in insurance coverage on self-reported overall health, physical functioning, and stroke, myocardial infarction, or death among participants in the Health and Retirement Survey, a longitudinal study of older adults sponsored by the National Institute of Aging. Methods: Data from the 92, 94, 96, and 98 Health and Retirement Survey will be analyzed. The study population will be individuals age 45-61 at the time of their 92 HRS interview who reported coverage by private insurance (N=7580) or who said they had no form of insurance (N=1700). The main independent variable in analyses will be either a) the number of periods without insurance (range 0-4 for HRS 92- 98) or b) change in insurance coverage (e.g. being insured in 92, uninsured in 94). Extensive data from the 92- 96 HRS interviews will be used to adjust for demographics, socioeconomic status, health status, and health behaviors so the independent effect of insurance coverage on health outcomes can be estimated. The main dependent variables will be self-reported overall health (SROH), physical functioning, and cardiovascular events or death. Using logistic and linear regression, we will analyze the relationship between 1) the number of periods without insurance and change in SROH and physical functioning between 92 and 96, 2) changes in insurance coverage between 92- 94 and changes in SROH and physical functioning from 92- 94 (with similar analysis for 94-96), and 3) the number of periods without insurance and new cardiovascular events and death between 92 and 98. Interactions between insurance and income, wealth, marital status and health will be analyzed too. Significance: This will be the first longitudinal, population-based study to determine the independent effect of lack of insurance on morbidity and mortality. It will identify the subgroups of the uninsured who are most at risk (e.g. low income, hypertension, diabetes) and who may be targets for interventions. As the number of uninsured continues to grow, information about health outcomes for the uninsured will be essential to guide future health programs and policies.